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Custom aggregate function guide
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---
title: Create custom aggregate functions
description: Create your own custom aggregate functions in Flux using the `reduce()` function.
v2.0/tags: [functions, custom, flux, aggregates]
menu:
v2_0:
name: Custom aggregate functions
parent: Create custom functions
weight: 301
---
To aggregate your data, use the Flux
[built-in aggregate functions](/v2.0/reference/flux/functions/built-in/transformations/aggregates/)
or create custom aggregate functions using the
[`reduce()`function](/v2.0/reference/flux/functions/built-in/transformations/aggregates/reduce/).
## Aggregate function characteristics
Aggregate functions all have the same basic characteristics:
- They operate on individual input tables and transform all records into a single record.
- The output table has the same [group key](/v2.0/query-data/get-started/#group-keys) as the input table.
## How reduce() works
The `reduce()` function operates on one row at a time using the function defined in
the [`fn` parameter](/v2.0/reference/flux/functions/built-in/transformations/aggregates/reduce/#fn).
The `fn` function maps keys to specific values using two [objects](/v2.0/query-data/get-started/syntax-basics/#objects)
specified by the following parameters:
| Parameter | Description |
|:---------: |:----------- |
| `r` | An object that represents the row or record. |
| `accumulator` | An object that contains values used in each row's aggregate calculation. |
{{% note %}}
The `reduce()` function's [`identity` parameter](/v2.0/reference/flux/functions/built-in/transformations/aggregates/reduce/#identity)
defines the initial `accumulator` object.
{{% /note %}}
### Example reduce() function
The following example `reduce()` function produces a sum and product of all values
in an input table.
```js
|> reduce(fn: (r, accumulator) => ({
sum: r._value + accumulator.sum,
product: r._value * accumulator.product
})
identity: {sum: 0.0, product: 1.0}
)
```
To illustrate how this function works, take this simplified table for example:
```txt
_time _value
----------------------- -------
2019-04-23T16:10:49.00Z 1.6
2019-04-23T16:10:59.00Z 2.3
2019-04-23T16:11:09.00Z 0.7
2019-04-23T16:11:19.00Z 1.2
2019-04-23T16:11:29.00Z 3.8
```
###### Input objects
The `fn` function uses the data in the first row to define the `r` object.
It defines the `accumulator` object using the `identity` parameter.
```js
r = { _time: 2019-04-23T16:10:49.00Z, _value: 1.6 }
accumulator = { sum : 0.0, product : 1.0 }
```
###### Key mappings
It then uses the `r` and `accumulator` objects to populate values in the key mappings:
```js
// sum: r._value + accumulator.sum
sum: 1.6 + 0.0
// product: r._value * accumulator.product
product: 1.6 * 1.0
```
###### Output object
This produces an output object with the following key value pairs:
```js
{ sum: 1.6, product: 1.6 }
```
The function then processes the next row using this **output object** as the `accumulator`.
{{% note %}}
Because `reduce()` uses the output object as the `accumulator` when processing the next row,
keys mapped in the `fn` function must match keys in the `identity` and `accumulator` objects.
{{% /note %}}
###### Processing the next row
```js
// Input objects for the second row
r = { _time: 2019-04-23T16:10:59.00Z, _value: 2.3 }
accumulator = { sum : 1.6, product : 1.6 }
// Key mappings for the second row
sum: 2.3 + 1.6
product: 2.3 * 1.6
// Output object of the second row
{ sum: 3.9, product: 3.68 }
```
It then uses the new output object as the `accumulator` for the next row.
This cycle continues until all rows in the table are processed.
##### Final output object and table
After all records in the table are processed, `reduce()` uses the final output object
to create a transformed table with one row and columns for each mapped key.
```js
// Final output object
{ sum: 9.6, product: 11.74656 }
// Output table
sum product
---- ---------
9.6 11.74656
```
{{% note %}}
#### What happened to the \_time column?
The `reduce()` function only keeps columns that are:
1. Are part of the input table's [group key](/v2.0/query-data/get-started/#group-keys).
2. Explicitly mapped in the `fn` function.
It drops all other columns.
Because `_time` is not part of the group key and is not mapped in the `fn` function,
it isn't included in the output table.
{{% /note %}}
## Custom aggregate function examples
To create custom aggregate functions, use principles outlined in
[Creating custom functions](/v2.0/query-data/guides/custom-functions)
and the `reduce()` function to aggregate rows in each input table.
### Create a custom average function
This example illustrates how to create a function that averages values in a table.
_This is meant for demonstration purposes only.
The built-in [`mean()` function](/v2.0/reference/flux/functions/built-in/tranformations/aggregates/mean/)
does the same thing and is much more performant._
{{< code-tabs-wrapper >}}
{{% code-tabs %}}
[Comments](#)
[No Comments](#)
{{% /code-tabs %}}
{{% code-tab-content %}}
```js
average = (tables=<-, outputField="average") =>
tables
|> reduce(
// Define the initial accumulator object
identity: {
count: 1.0,
sum: 0.0,
avg: 0.0
}
fn: (r, accumulator) => ({
// Increment the counter on each reduce loop
count: accumulator.count + 1.0,
// Add the _value to the existing sum
sum: accumulator.sum + r._value,
// Divide the existing sum by the existing count for a new average
avg: accumulator.sum / accumulator.count
})
)
// Drop the sum and the count columns since they are no longer needed
|> drop(columns: ["sum", "count"])
// Set the _field column of the output table to to the value
// provided in the outputField parameter
|> set(key: "_field", value: outputField)
// Rename avg column to _value
|> rename(columns: {avg: "_value"})
```
{{% /code-tab-content %}}
{{% code-tab-content %}}
```js
average = (tables=<-, outputField="average") =>
tables
|> reduce(
identity: {
count: 1.0,
sum: 0.0,
avg: 0.0
}
fn: (r, accumulator) => ({
count: accumulator.count + 1.0,
sum: accumulator.sum + r._value,
avg: accumulator.sum / accumulator.count
})
)
|> drop(columns: ["sum", "count"])
|> set(key: "_field", value: outputField)
|> rename(columns: {avg: "_value"})
```
{{% /code-tab-content %}}
{{< /code-tabs-wrapper >}}
### Aggregate multiple columns
Built-in aggregate functions only operate on one column.
Use `reduce()` to create a custom aggregate function that aggregates multiple columns.
The following function expects input tables to have `c1_value` and `c2_value`
columns and generates an average for each.
```js
multiAvg = (tables=<-) =>
tables
|> reduce(
identity: {
count: 1.0,
c1_sum: 0.0,
c1_avg: 0.0,
c2_sum: 0.0,
c2_avg: 0.0
}
fn: (r, accumulator) => ({
count: accumulator.count + 1.0,
c1_sum: accumulator.c1_sum + r.c1_value,
c1_avg: accumulator.c1_sum / accumulator.count,
c2_sum: accumulator.c2_sum + r.c2_value,
c2_avg: accumulator.c2_sum / accumulator.count
})
)
```
### Aggregate gross and net profit
Use `reduce()` to create a function that aggregates gross and net profit.
This example expects `profit` and `expenses` columns in the input tables.
```js
profitSummary = (tables=<-) =>
tables
|> reduce(
identity: {
gross: 0.0,
net: 0.0
}
fn: (r, accumulator) => ({
gross: accumulator.gross + r.profit,
net: accumulator.net + r.profit - r.expenses
})
)
```